Research Resolving Polychlorinated Biphenyl Source Fingerprints in Suspended Particulate Matter of San Francisco Bay G L E N N W . J O H N S O N , * ,† WALTER M. JARMAN,† CORINNE E. BACON,† JAY A. DAVIS,‡ ROBERT EHRLICH,† AND ROBERT W. RISEBROUGH§ Energy and Geoscience Institute, Department of Civil and Environmental Engineering, University of Utah, 423 Wakara Way, Suite 300, Salt Lake City, Utah 84108, San Francisco Estuary Institute, 180 Richmond Field Station, 1325 South 46th Street, Richmond, California 94804, and The Bodega Bay Institute, 2711 Piedmont Avenue, Berkley, California 94705
The presence of PCB contamination in San Francisco Bay has been documented, but the number of sources, their chemical composition, and their geographic/temporal distribution are poorly understood. A self-training pattern recognition technique, polytopic vector analysis is used to determine those parameters from PCBs adsorbed on the particulate fraction of surface waters. Five chemical fingerprints (end-members) were resolved. Four were consistent with published Aroclor patterns. Aroclor 1260 was observed throughout the estuary, in all cruises, with highest proportions observed in Coyote Creek, a tributary of the South Bay. A pattern that matches typical Aroclor 1254 was observed in all cruises but was in generally higher abundance in spring 1995. A second Aroclor 1254 pattern, consistent with an atypical Aroclor 1254 batch described in the literature, was observed in moderate proportions in the three 1996 cruises. Aroclor 1248 was present in significant proportions in only one cruise (cruise 12: July 1996) but was the dominant fingerprint in the Central Bay samples collected at that time. End-member 5 did not match published Aroclor source patterns. Its composition exhibits high proportions of the metabolism-resistant congeners PCB-138 and PCB-153. The source of this pattern is not known, but we hypothesize that it may be due to sewage inputs in the Bay or from atmospheric inputs.
Introduction Identification of chemical contaminant sources in the environment is a common objective of environmental investigations. Given multiple sources in a field area, the environmental scientist is faced with the challenge of mapping multiple plumes with overlapping geographic * Corresponding author phone: (801)581-6151; e-mail: gjohnson@ egi.utah.edu. † University of Utah. ‡ San Francisco Estuary Institute. § Bodega Bay Institute. 552
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distributions. If a source is characterized by a distinctive spectrum of analytes, then the problem may be addressed through a multivariate statistical approach (1-4). The possibility of multiple sources implies that many samples may have been impacted by more than one source. This necessitates a method capable of resolving mixtures. The three parameters of interest in a mixing system are (1) the number of sources in the system; (2) the chemical composition of each source; and (3) the contribution of each source in each sample. In complex environmental systems such as polychlorinated biphenyls (PCBs) in urban estuaries, source identification is confounded by complexities which include (1) limited knowledge of contaminant sources; (2) variable compositions within a source category; and (3) physical, biological, and/or chemical alteration processes. The situation is too complex for one to make a priori assumptions regarding source and alteration patterns. Attempts at multivariate classification of PCB patterns using training data set libraries have met with limited success (1). In light of such complexities another strategy involves utilization of a class of procedures termed exploratory data analysis. Such methods allow patterns and correlations to be derived directly from the analysis of ambient data (5) and do not require a priori hypotheses. The advantage of such an approach is that we have the opportunity to be surprised; to learn something unexpected about the system. One candidate algorithm for use in such environmental problems is polytopic vector analysis (PVA), a multivariate statistical technique developed in mathematical geology for analysis of mixtures (6-14). PVA requires no a priori assumption of the number or identity of chemical fingerprints. In this study we apply PVA in the analysis of polychlorinated biphenyl sources in San Francisco Bay. PVA is the result of an evolution of algorithms developed over a period of 30 years, and that development was guided primarily by heuristics. It was not developed based on a series of proofs and theorems nor on the inevitability of a tractable solution based on knowledge of data distributions and probability. Therefore, the best way to test its validity is to apply it to complex but well-understood environmental systems. The application to PCBs in San Francisco Bay satisfies this criterion. The congener-specific composition of many PCB sources has been documented (15-17). PCBs are relatively persistent in the environment and thus retain the chemical character of the source longer than many other chemicals. Source patterns can however be altered in the environment by a variety of physical and biological mechanisms (18-28). Therefore, the extent to which source profiles coming from the analysis match known source profiles and alteration patterns provides an external check on the validity of the approach. San Francisco Bay Environmental Setting. PCBs are one of a number of contaminants of concern that have been identified in San Francisco Bay. Patterns and trends of PCBs in the Bay have been documented in a number of studies (4, 29-36). Elevated levels of PCBs were first identified in biota from San Francisco Bay in 1969 (29). The impact of organochlorine contaminants on the ecology of the bay and estuary has been the focus of several subsequent programs (4, 30-36). The ongoing Regional Monitoring Program for Trace Substances (RMP) was initiated in 1993 and monitors contaminant concentrations in water, sediments, and biota in the estuary (34-36). Jarman et al. (4) and Risebrough (34) 10.1021/es981246v CCC: $19.00
2000 American Chemical Society Published on Web 01/08/2000
FIGURE 1. Location of RMP sampling stations in San Francisco Bay.
FIGURE 2. Combined river outflow for Sacramento and San Joaquin River delta (40). have presented reviews of RMP and other historical PCB data and have discussed temporal trends within the Bay going back to the 1970s. Both noted that there has not been a decline in PCB levels in the Bay since the middle 1970s, suggesting ongoing contributions of PCBs to the system. San Francisco Bay is a shallow turbid estuary consisting of three subareas: the Northern Reach (San Pablo Bay, Carquinez Straits, Honker Bay, Grizzly Bay, and the Sacramento-San Joaquin River Deltas), the Central Bay, and the South Bay (Figure 1). The Northern Reach and South Bay are hydrologically distinct. The northern reach is a partially mixed estuary with primary freshwater inflow via the Sacramento and San Joaquin Rivers. South Bay is a seasonal lagoon, much more tidally influenced, and with a much lower freshwater
input (37-39). The San Joaquin and Sacramento Rivers provide the majority of freshwater volume to the Bay (37). Upstream of the delta however, these rivers are constrained by a series of dams, which limit their suspended solid input to the Bay. Therefore, the largest suspended particulate concentrations are consistently observed at Petaluma River/ San Pablo Bay sampling stations in the Northern Reach and in extreme South Bay sampling stations (4, 34-36). Freshwater inflow to the bay is highest in the winter and early spring (Figure 2). Seasonal differences result in marked contrasts in hydrodynamic and biological processes in the Bay, which may have an effect on PCB patterns and concentrations. VOL. 34, NO. 4, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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Experimental Section PCB Determination. This study focuses on data from a series of cruises conducted in 1995 and 1996 as part of the RMP. These data were chosen because consistent field and laboratory methods were used (34, 35). The five RMP cruises were conducted April 1995 (cruise 8), August 1995 (cruise 9), February 1996 (cruise 10), April 1996 (cruise 11), and July 1996 (cruise 12). Samples were collected approximately 1 m below the water surface using Teflon tubing (3/4”) attached to an aluminum pole oriented up current from the vessel and upwind from equipment and personnel. The vessel was anchored and the engines turned off. Approximately 100 L were collected at each station. Station locations are shown in Figure 1. Particulate fractions of estuary water (the subject of this paper) were collected by pumping water through a 293-mm glass fiber filter designed to isolate the >1 µm particulate fraction. Discussion of patterns and trends in the dissolved and particulate phases has been presented by Jarman et al. (4). The entire sampling system was thoroughly rinsed with methanol prior to sampling, and an all-Teflon-stainless steel system further minimized potential contamination. During sampling, the system was closed to outside sources of contamination, and extreme caution was taken to minimize introduction of contaminants. Custom-built Soxhlets were used to extract organics from both plugs and filters (acetone extraction followed by hexane). Water was removed by partitioning with hexane in a separatory funnel. The extracts were reduced to 1-2 mL for cleanup. Florisil columns were used to separate each extract into three different fractions (34, 35). Fraction 1 contained PCBs and DDE, fraction 2 contained organochlorine pesticides, and fraction 3 contained the more polar pesticides (e.g., Diazinon). Prior to analysis each sample was spiked with an internal standard to account for volume differences among samples. The PCB-containing fractions were analyzed on a Hewlett-Packard (HP) 5890 series II gas chromatograph (GC) equipped with a 63Ni electron-capture detector and a HP 7673A automatic sampler. Two 60 m, 0.25 mm i.d., 0.25 mm (film thickness), DB-5 and DB-17 columns (J&W Scientific) were used to provide dual column confirmation. Only particulate phase PCB values are reported in this paper. Sampling and analytical methods are discussed in more detail in the RMP Annual Reports (34, 35). Data Screening. The initial data set for particulate phase congener specific PCB analyses consisted of 39 congeners measured in each of 76 samples. A preliminary data analysis involving principal components analysis (an intermediate step in PVA) and evaluation of numerical goodness-of-fit indices and graphical diagnostics was performed (7, 14). Samples of low total PCB concentration demonstrated a poor fit by the principal components model. This is likely a product of low precision as the method detection limits are approached. Therefore, all samples with a total PCB concentration less than 150 pg/L (18 of 76 samples) were removed from the matrix. The data were further reduced by removal of analytes reported as nondetect (ND) in more than 10% of the samples, resulting in removal of eight of 39 analytes. In a previous analysis of a portion of the data reported here, Jarman et al. (4) noted a strong compositional difference between cruises. That observation was used as justification for presentation of a mixing model based on only one subset of that data (cruise 8). In this present study, several cruisespecific QA/QC problems were recognized. The identification and deletion of key analytes responsible for the intercruise variation has allowed development of a single, multicruise model. PCB-156 had coelution problems in cruise 11 samples. Cruise 8 samples yielded consistently higher concentration of PCBs 174, 177, and 203, a bias also attributed to coelution. These four analytes were removed from the analysis. 554
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Two individual samples were removed from the data set because of matrix interference/coelution. Sample BD40/ cruise 12 was identified as an outlier due to anomalously high concentrations of PCB206. Sample BW10/cruise 12 was an outlier due to anomalously high concentrations for a number of congeners. Subsequent review of the data indicated the presence of matrix interference/coelution. The problem was uncorrectable, and the two samples were removed from the matrix. Finally, data screening identified several correctable problems, such as data entry errors. In these instances, the data were corrected and resubmitted for statistical analysis. In some instances, outliers and other observed systematic lack-of-fit could not be confidently explained. In these cases no deletions were made. The final matrix submitted for model resolution by PVA was composed of 56 samples and 27 congeners. Polytopic Vector Analysis. The PVA algorithm has evolved over a period of 30 years, primarily within the mathematical geology literature (6-14). The formalism of the PVA algorithm is outlined in detail by Johnson (14). PVA is designed to determine the three parameters of a mixing system: (1) the number of source fingerprint (herein termed “end-members”); (2) the chemical composition of each end-member; and (3) the relative proportions of each end-member in each sample. These objectives are identical to those defined in air quality receptor modeling. In that context, end-member compositions are termed “source compositions”, and mixing proportions are termed “source contributions”. PVA was performed using software code written by the corresponding author in the programming language MATLAB (The Mathworks, Inc., Natick, MA, U.S.A.). The code was written based on the original FORTRAN algorithms of Klovan and Miesch (9) and Full et al. (10, 11). PVA is performed in two steps. The first is a principal components analysis performed on the transformed matrix. Evaluation of the number of significant principal components to retain for the mixing model and evaluation of outliers are done using the goodness-of-fit criteria of Miesch (7) and Johnson (14). The second step in PVA modeling determines the chemical composition of the end-members and the relative proportions of each end-member in each sample. This step uses the DENEG algorithm of Full et al. (10, 11), an iterative algorithm with explicit nonnegativity constraints performed in reduced dimensional principal component space. The constraints require that the end-member composition and mixing proportion matrices have no negative elements. Slight negative end-member mixing proportions are permitted, so as to allow for some noise in the data. The default negativity tolerance is -5% (10). PVA is a self-training classifier. No training data set of known or suspected end-member compositions is required to develop a mixing model. Uncertainty Analysis. A bootstrap method was used to assess the uncertainty in the resolved end-member compositions and mixing proportions. The original 56-sample data matrix was randomly resampled 100 times using the resampling method of Henry (41). For each resampled matrix, PVA was run for a five end-member model. The default negativity tolerance of -5% was used in each model. The algorithm was allowed to iterate up to 50 times. If the model converged (i.e., all sample mixing proportions were within the -5% criterion), the convergent iteration was taken for that matrix. If the model failed to converge, the iteration that came closest was taken for that matrix.
Results and Discussion Congener-specific PCB results for dissolved and particulate phases (as well as water quality parameters and results for other trace substance analyses) are reported in the RMP annual reports (34, 35). Jarman et al. (4) presented an analysis of patterns and trends in both particulate and dissolved
TABLE 1. End-Member Compositions Matrixa variables
FIGURE 3. Goodness-of-fit scatter plots and Miesch coefficientsof-determination for PCB-138. Scatter plots are shown for 2-5 endmembers. Scatter plots for all congeners are made available in Supporting Information. phases. In particular, Jarman et al. (4) included discussions of particulate/dissolved phase partitioning, the relationship of dissolved/particulate ratios to total suspended solids, temporal trends in the data, and comparison of these data to previous PCB investigations in the Bay. Descriptive statistics for cruise-by-cruise particulate PCB concentrations are provided in Supporting Information. Determination of Number of End-Members/Goodnessof-Fit. The number of end-members was determined using the normalized loadings criterion described by Ehrlich and Full (12), inspection of goodness-of-fit indices and scatter plots as outlined by Miesch (7) and Johnson (14). For the first five principal components, normalized loadings exceeded the default 0.100 criteria in more than 40 of the 56 samples. For principal components greater than five, no more than one sample exceeded the 0.100 criterion. This strongly suggested a five end-member model. Back-calculation scatter plots were evaluated for each congener, for each potential number of end-members. An example scatter-plot series for PCB-138 is shown in Figure 3. The x-axis of each plot shows the measured amount of each congener in percent metric. The y-axis shows the value as back-calculated for the indicated number of end-members. The 45° line that bisects each plot shows a line of perfect fit for measured versus back-calculated data. Given a perfect fit, all data points will plot on the 1:1 line, and the Miesch coefficient of determination (7) will equal 1.0. Figure 3 shows a poor fit for PCB138 for 2-4 end-members. For five endmembers, a CD of 0.88 is attained, and the observed lackof-fit is uniformly distributed about the 1:1 line. A good fit was observed for most analytes for a five endmember model. Exceptions were noted for four congeners: PCB 87, PCB 95, PCB 132, and PCB 151. The poor fit for PCBs 87 and 151 are due to the leverage of a few outlier samples. No error or quantitation problem could be identified for these outliers, and as such, the data were left unaltered. The poor fit samples for PCB 132 are predominantly in samples from cruise 11 (April 1996). No cause of the systematic bias in PCB132 could be verified for cruise 11, so the data were left intact. The fit problem with PCB095 (Miesch CD ) 0.45)
end-members
IUPAC
structure
EM-1
EM-2
EM-3
EM-4
EM-5
PCB 18 PCB 28 PCB 31 PCB 44 PCB 70 PCB 74 PCB 87 PCB 95 PCB 97 PCB 99 PCB101 PCB105 PCB110 PCB118 PCB128 PCB132 PCB138 PCB149 PCB151 PCB153 PCB158 PCB170 PCB180 PCB183 PCB187 PCB195 PCB206
25-2 24-4 25-4 23-25 25-34 245-4 234-25 236-25 245-23 245-24 245-25 234-34 236-34 245-34 234-234 234-236 234-245 236-245 2356-25 245-245 2346-34 2345-234 2345-245 2346-245 2356-245 23456-234 23456-2345
11.17 13.49 13.68 13.55 18.67 9.78 2.82 4.28 1.80 0.00 1.63 1.43 2.55 0.00 0.00 1.69 0.00 0.05 0.88 0.00 0.04 0.79 1.51 0.00 0.00 0.20 0.00
0.00 0.41 0.00 2.27 5.57 2.22 5.10 5.77 4.15 6.77 13.44 0.00 14.78 8.18 2.63 0.00 0.00 5.35 1.84 12.95 0.93 0.00 0.00 0.77 4.23 0.00 2.64
0.00 2.11 0.61 1.46 6.92 2.33 3.59 0.00 4.53 6.42 7.96 10.49 9.89 21.45 3.48 0.40 0.00 1.51 2.18 8.29 0.28 1.07 0.00 0.85 3.68 0.00 0.49
0.58 0.70 1.18 0.27 0.00 0.00 0.40 5.36 0.00 0.00 0.00 0.00 1.46 0.23 0.87 6.14 11.76 11.35 4.19 12.63 0.79 8.57 17.10 3.36 9.20 1.95 1.91
0.00 1.94 3.67 0.00 0.00 0.12 0.00 4.30 1.13 2.85 6.46 0.00 4.36 8.82 0.05 3.06 22.48 9.80 0.89 15.05 1.83 0.75 7.10 1.50 3.46 0.00 0.37
a
In percent.
appears to be related to bias in cruise 8 samples, which are consistently back-calculated at too high a concentration. It is possible that the levels of these congeners were biased by either coelution with another congener (e.g., PCB 132/105 (34)) or by a contaminant. With the exception of the four congeners noted above, all other congeners displayed CDs exceeding 0.50, and the observed lack-of-fit was uniform about the 1:1 line on the scatter plots. CDs and scatter plots for all analytes are presented in the Supporting Information. A five end-member model can be supported by this data set. The low CDs and lack-of-fit observed for the four congeners discussed above suggests the possibility of additional endmembers, but the residual variance associated with such fingerprint(s) do not permit their characterization. End-Member Compositions and Geographic Distributions. As per explicit constraints of the algorithm, endmember compositions were within the default 5% negativity tolerance allowed by the algorithm. The geographic/temporal distributions of the mixing proportions are shown in the map array presented as Figure 4. End-member compositions are shown graphically in Figures 5 and 6. The height of the bar indicates the percentage of the congener in the original model. The error bars with associated midpoint indicates the mean and standard deviation of the end-member compositions based on the bootstrap uncertainty analysis. The results of the uncertainty analysis are available in numerical form in the Supporting Information. The variability of individual analytes in the end-member compositions is in the range of (1.5% for most congeners. The worst case standard deviation based on the uncertainty analysis was (4.9%. End-members 1-4 were very similar to published Aroclor compositions. End-member 1 closely matches published patterns for Aroclor 1248 (Figure 5). It is virtually absent in samples from cruises 8-11 but appears suddenly in high proportions in cruise 12 (July 1996), predominantly in Central Bay samples. VOL. 34, NO. 4, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 2. End-Member Mixing Proportions Matrixa sample cruise
station
EM-1 1248
EM-2 T 1254
EM-3 A 1254
EM-4 1260
EM-5 alteration
8 8 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 10 10 10 10 10 10 10 10 10 10 10 11 11 11 11 11 11 11 11 11 11 11 11 12 12 12 12 12 12 12 12 12 12 12 12
BA10 BA30 BA40 BB70 BC10 BC60 BD15 BD20 BD30 BD40 BD50 BF20 BA10 BA30 BA40 BD15 BD20 BD30 BD40 BD50 BF20 BA05 BA10 BA30 BA40 BB70 BC10 BC60 BD20 BD30 BD60 BW10 BA05 BA10 BA30 BA40 BB70 BC10 BD20 BD30 BD40 BD50 BD60 BW10 BA05 BA30 BA40 BB70 BC20 BC60 BD20 BD30 BD40 BD50 BD60 BF20
2% -2% -3% 1% 3% 1% -1% 3% 5% 4% 5% 1% 6% 2% 3% 2% 6% 4% 8% 3% 8% 10% 4% 1% -1% 2% 4% 4% 9% 3% 1% 2% -2% 4% -0% 0% 5% 1% -0% 5% 2% 2% 1% -1% 15% 37% 6% 45% 66% 69% 22% 7% 6% 7% 4% 6%
34% 33% 34% 39% 35% 35% 40% 35% 38% 36% 37% 34% 2% 5% -1% 12% 3% 13% 13% 11% 12% 18% 19% 17% 15% 20% 18% 20% 30% 23% 17% 12% 12% 22% 14% 17% 29% 29% 23% 20% 18% 23% 19% 17% 29% 11% 19% 13% 5% 6% 18% 26% 22% 23% 25% 20%
3% 5% 3% 1% -1% -2% 2% 2% -1% 3% 1% 0% 22% 20% 24% 17% 19% 13% 11% 15% 11% 16% 20% 24% 22% 19% 20% 24% 16% 19% 21% 3% 29% 27% 32% 27% 15% 15% 28% 21% 26% 20% 24% 9% 13% 24% 24% 21% 17% 14% 23% 20% 21% 21% 17% 20%
32% 33% 33% 30% 29% 30% 35% 36% 28% 33% 39% 41% 30% 32% 30% 33% 27% 22% 23% 20% 23% 42% 43% 41% 49% 43% 43% 45% 29% 41% 46% 77% 54% 36% 38% 37% 25% 30% 32% 32% 33% 34% 41% 76% 30% 12% 34% 10% -4% -5% 24% 30% 31% 31% 34% 35%
30% 31% 32% 30% 35% 35% 24% 24% 30% 25% 19% 24% 40% 41% 45% 37% 45% 48% 45% 52% 47% 14% 14% 17% 15% 16% 14% 7% 16% 14% 15% 6% 7% 11% 16% 20% 26% 25% 17% 22% 20% 21% 15% -1% 12% 15% 16% 11% 17% 17% 12% 18% 20% 17% 20% 19%
a
In percent.
End-member 2 shows a pattern similar to typical Aroclor 1254 (Figure 5). It is present in highest proportions in cruise 8 samples but is observed in moderate proportions in all cruises. End-member 3 closely resembles an atypical Aroclor 1254 that has been reported in the literature (Figure 5). Frame et al. (15) reported this pattern in an Aroclor 1254 standard obtained from AccuStandard (Frame sample A4/AccuStandard Lot 6024). The congener pattern in this sample was remarkably different from other 1254 standards in Frame’s study and from previously published 1254 patterns. As suggested by Frame et al. (15), atypical Aroclor 1254 likely represents a product synthesized by a different chlorination process. Its occurrence in San Francisco Bay suggests that 556
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its use may have been wider than previously thought. AccuStandard now provides the atypical Aroclor 1254 standard only when specifically requested (AccuStandard, personal communication). The EM-3 pattern is largely limited to samples collected during the final three cruises of this study (cruises 10-12). End-member 4 closely matches published patterns of Aroclor 1260 (Figure 5). It is a significant contributor to the bay in all cruises. The highest EM-4 mixing proportions (greater than 70%) are observed in BW10 samples in cruises 10 and 11. BW10 is the southernmost sampling station in the study, near Milpitas, CA, and is located well upstream in Coyote Creek. The absence of this pattern in Coyote Creek during cruises 8, 9, and 12 does not necessarily imply that it was restricted to cruises 10 and 11. BW10 was not part of RMP sampling during cruises 8 and 9, and multiple matrix interference peaks forced removal of cruise 12/BW10 from the matrix used for this analysis. End-member 5 did not closely match any known Aroclor patterns. Its origin is unknown at this time, but we provide the following observations and hypotheses. The pattern is closest to Aroclor 1260 (Figure 6), but it departs from that pattern in that it is enhanced in congeners 138 (234-245 PCB) and 153 (245-245 PCB). It may be the product of some sort of alteration mechanism. One anaerobic dechlorination process (Process N dechlorination (19)) involves removal of flanked meta chlorines and preferentially acts on Aroclor 1260. Process N is known to reduce PCB 170 and 180 and increase PCB 138, but dechlorination of other congeners results in a pattern that does not closely match EM-5 (19). Patterns similar to EM-5 have been reported in ambient samples of urban (42) and marine (43) air, suggesting the possibility of an atmospheric source. If such were the explanation for this fingerprint, then its predominance during summer cruise, at a time of low fresh water inflow is logical. However, in these two studies the pattern was observed in only a few anomalous samples. The majority of samples in those studies did not match this pattern. PCBs 138 and 153, the dominant congeners in EM-5, have been shown to be resistant to metabolism in higher organisms. These congeners are often observed in proportionally high levels in whole biota and tissue samples (24-26), and they are the dominant congeners in fish and bivalve assays in San Francisco Bay (34-36). However, unlike EM-5, patterns in biota typically display higher concentrations of PCB 153 than 138. PCBs 138 and 153 are also often reported as the dominant congeners in feces, sewage sludge, and sludge treated soils (26-28). In these studies, the concentration of PCB 138 is often higher than that of PCB 153. The hypothesis that this pattern is related to sewage input is consistent with several lines of evidence. Sewage outfalls are a ubiquitous source of input waters to the Bay, which would explain the presence of EM-5 in samples from all cruises (Figure 4). The highest proportional contributions were observed in cruise 9 (August, 1995: Figure 4) at a time of low tributary inflow. This is reasonable. If suspended particulate matter from tributaries were the primary source of “fresh” Aroclor patterns then one would expect a more constant, less seasonal source (sewage outfalls) to be dominant at times of low freshwater inflow. Risebrough (34) presented preliminary PCB mass balance calculations for San Francisco Bay. Risebrough suggested that wastewater could be the dominant ongoing source of PCBs in the Estuary. The similarity of end-member 5 to patterns observed in feces and sewage sludge suggests that at certain times in the estuary this hypothesis may be true. However, the presence of other patterns with striking similarity to unaltered Aroclors clearly suggests the importance of other sources. A sewage effluent study, including congener specific PCB analyses, is presently being conducted
FIGURE 4. Bubble-map array for mixing proportions of five end-members in each of five cruises. The size of each circle (bubble) is proportional to the mixing proportion of the indicated end-member. The maximum bubble value for each map is indicated at the bottom of each. by the San Francisco Estuary Institute and should shed light on this hypothesis. The match of EM-1 through EM-4 to known Aroclor patterns (Figure 5) is striking. For Aroclors 1254 and 1260 (end-members 2-4) the freshest Aroclor patterns are generally present in the winter and spring cruises (cruise 8, 10, and 11): times of high freshwater inflow and associated high total suspended solids (Figure 2). This suggests that the ongoing source of PCBs in San Francisco Bay is the result of resuspension of sediment in the bay and its tributaries. The Aroclor 1248 fingerprint (end-member 1) is an exception. This pattern was virtually absent prior to cruise 12. In July 1996, it appears with particularly high contributions in the central Bay. This relatively sudden appearance at a time of low tributary outflow (Figure 2) suggests a more temporally
restricted release or spill occurring between May and July 1996. This analysis provides insight into the sources of PCBs in San Francisco Bay as well as a regional context for further investigations. It does not however answer all questions. Our identification of Aroclor 1248, 1254, and 1260 sources does not clearly identify the specific origin of these inputs. Identification of specific sources will result only through extensive compilation of news, facts, and data from more geographically focused studies. Further, our hypotheses regarding the origin of end-member 5 are based on examination of recent literature. Hopefully, further studies will shed light on the true origin of this pattern. The results of this study demonstrate the utility of PVA in analysis of chemical data in complex environmental VOL. 34, NO. 4, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 5. Fingerprint compositions for end-members 1-4 (left) compared to analogous Aroclor compositions (right). Aroclor congener data from Frame et al. (15). Bar heights of end-members are shown for PVA model of the full 56 sample data set. Error bars indicate the mean and standard deviation of compositions based on the bootstrap uncertainty analysis.
Acknowledgments This work was funded by the San Francisco Estuary Institute. We thank Andy Gunther of Applied Marine Sciences and Russ Flegal (W.I.G.S. at U.C. Santa Cruz), who have supported this program since its inception. We are grateful to George Frame for providing information and insight on the atypical Aroclor 1254 pattern. Thank you to Terry Biddleman, John Quensen, and Matt Zwiernik for discussions of possible PCB alteration processes. Thanks also to Gareth Thomas for sharing unpublished data. We thank Robert Ramer, Molly Jacobs, Jocelyn Vedder, Gordon Smith, and Jordan Gold for sample collection and laboratory analysis.
Supporting Information Available
FIGURE 6. Comparison of congener composition of Aroclor 1260, end-member 4 and end-member 5. Aroclor congener data from Frame et al. (15). Bar heights of end-members are shown for PVA model of the full 56 sample data set. Error bars indicate the mean and standard deviation of compositions based on the bootstrap uncertainty analysis. systems. PVA was applied to a well-studied class of chemicals in a well-studied estuarine system. The resultant fingerprints were resolved without use of a training data set and without a priori hypothesis of PCB sources and alteration mechanisms. The patterns show good correlation to known Aroclor source patterns and to an altered PCB fingerprint similar to patterns reported in the literature in sewage and atmospheric samples. 558
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Figures of CD scatter plots for 2-5 end-member models and tables of descriptive statistics for cruise-by-cruise particulate PCB concentrations and end-member compositions from bootstrap uncertainty analysis. This material is available free of charge via the Internet at http://pubs.acs.org.
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Received for review December 1, 1998. Revised manuscript received November 4, 1999. Accepted November 23, 1999. ES981246V
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